We rely on a Hamiltonian Monte Carlo (HMC) [1]}, [2]}, [3]}-within-Gibbs algorithm, with HMC used to sample the interaction parameters \(\mathbf {\Psi }_{uv}\)  for \(1 \le u < v \le p\) , and other parameters updated in Gibbs steps. Although this approach is highly effective in our experiments, it is important to carefully choose the step size \(e_0\)  and step length \(L_0\)  in HMC. In practice, we found \(e_0 \approx 0.01\)  and \(L_0 \approx 10\)  to work well.
